Federated learning (FL) is a framework for training machine learning models with the local data of users by repeatedly communicating the model weights and gradients between a server and a group of users [1]}. FL enables training models without users having to share their data with the server or other users and, hence, is a natural solution for training UA models. Training UA models in the federated setting, however, poses unique challenges described in the following.
